Coming Soon: Gorgias, a Help Desk AI Lawyers Could Use

While many lawyers are still debating yesterday’s hot-button technology issues like the cloud or LegalZoom, there is a far more urgent, more revolutionary, and possibly more controversial tech topic that is rapidly emerging: artificial intelligence.

You are no doubt wondering whether you might someday use artificial intelligence in your law practice.

I’ll make it easy on you: The answer is yes, and the time to invest in AI is ASAP. The technology is ready, and the lawyer who starts harnessing AI today will have the smartest machine on the block a year from now when others are stumbling out of the gate.

How the Tech Behind Machine Learning Works

Gorgias, reported by TechCrunch, is touted as “an artificial intelligence-powered help desk to make you much more efficient when it comes to answering customer support requests.” Right now Gorgias is still in private beta so we don’t know exactly what its capabilities will be, but based on their documentation and the state of AI technology, we can make some educated guesses.

Here’s my (grossly simplified) sense of how it will work: Let’s say you run Mousetrap.io, a business that builds and sells a better mousetrap. You set up your helpdesk@mousetrap.io address, sign up for a Gorgias account, and point your email to the Gorgias AI system.

The first time a customer asks a question about installing Mousetrap, Gorgias will know nothing about setting up your product, so it routes the email to you for an answer. You write a detailed response, attach Ikea-style documentation for proper Mousetrap installation procedures, and send your reply through the Gorgias system. Gorgias tracks the interaction, and can connect via API to your sales or payment processing system to lookup that customer’s records and even sync transcripts of your support interaction.

Now let’s say your next customer writes in with a similar installation question, but has a special concern about her pets. Gorgias’s AI has used your last interaction to sense how to recognize the customer’s installation query, but it doesn’t know how to deal with the pets part. So again it routes the email to you, but this time, it auto-populates your response with your previous installation answer. You add in some new info about pets and again send your reply through the Gorgias system. Voila! Gorgias has learned something about installations involving pets.

Then one day a customer writes in wondering how to install Mousetrap so that it won’t interfere with his Roomba. Gorgias isn’t sure what a Roomba is, but it guesses that it might be the name of a pet. So it routes the email to you and prepopulates it with your newly pet-friendly installation instructions.

Stupid Gorgias, you think, it can’t even tell the difference between a pet and a personal robot. So you delete the pet language and write up instructions about how to avoid Roomba-Mousetrap conflicts and send it through Gorgias. And now Gorgias knows that it needs to handle Roombas differently than it handles doggies.

You can see where this is going. The AI is dumb at first—you’ll do all the heavy lifting for any new query. But the machine learns quickly, and it never forgets. It may be six months before you get another Roomba question, and if it came to your direct email you’d probably have to dig around for your answer or figure out the whole thing anew. Not so with Gorgias; It will instantaneously suggest your previous answer and you’ll be on your way.

So what happens when Roomba specs have changed in the last six months? Well if you know about the change, no problem—you just edit your response, Gorgias learns the new answer, and you wait for the next Roomba question to come in. But what if you don’t know whether Roomba specs have changed or not? This is where it could get really good.

You can get yourself over to the Roomba website and see when the last code update was. Or, better yet, you can show Gorgias where that page is and what to look for. Now, when Gorgias routes the next Roomba / Mousetrap question to you, it also includes a flag saying, “The Roomba specs have changed three times since you last answered a question like this, would you like to see the details?” You click a link, review the changes, update your response, and route it through Gorgias. Not only did Gorgias just get smarter, it prevented you from making a mistake.

Better still, Gorgias knows every one of your customers who has previously contacted you with Roomba questions. If you learn that Roomba’s changed specs will require those customers to update their Mousetrap configurations, you can easily act on it. Just put together a “Critical Update” notice and use Gorgias to send it off to every one of your customers tagged as Roomba users. That way you provide important information to those who need it without bothering anyone who doesn’t.

What Gorgias Means for Lawyers

Now re-read that entire hypothetical and replace “Mousetrap” with a website privacy policy you wrote. Think of the “Roomba” documentation as California’s legislative update site for privacy laws. See how this will work in the law? Of course, Mousetrap could represent any situation where you’ve delivered a piece of legal work that is subject to questions and/or change.

I’m not saying Gorgias itself will be the answer to legal AI, but there are existing tools like Zendesk and Desk.com that already do at least some of the things I’ve described. They aren’t specifically targeted to lawyers, but that’s a poor reason to dismiss them; we are a customer service business after all.

And a few law-specific tools are rapidly maturing: ROSS uses IBM’s Watson framework to assist with legal research, while Beagle.ai leverages machine learning to predict your contract review preferences and streamline your redline process. They are just the start. Over the past few months, Google’s Tensor Flow, Facebook’s Big Sur, the Amazon Machine Learning platform, and Elon Musk’s non-profit OpenAI have all made powerful machine learning tools openly available, often for free. Legal technology vendors are certain to harness those resources to empower our machine-assisted future.

The tools won’t be perfect at first, but they’ll learn quickly—at first handling your most mundane tasks but evolving to deal with more complex situations. Both the software’s capabilities and the machine’s processing power will continuously improve, allowing AI systems to learn more, and faster, than you ever dreamed possible. If you don’t start figuring out how to harness it to your advantage, your competitors most certainly will.